This paper uses hedonic regression techniques to decompose the price of a house into land and structure components using real estate sales data for Tokyo. To get sensible results, a nonlinear regression model using data that covered multiple time periods was used. Collinearity between the amounts of land and structure in each residential property leads to inaccurate estimates for the land and structure value of a property. This collinearity problem was solved by using exogenous information on the rate of growth of construction costs in Tokyo in order to get useful constant-quality subindices for the price of land and structures separately.
